Deploying a custom Docker container to an Azure ML batch endpoint can be tricky due to limited documentation. However, see if the steps below will help you:
Prepare Your Docker Image: Ensure your Docker image is ready and pushed to a container registry accessible by Azure (e.g., Azure Container Registry).
Create an Azure ML Workspace: If you haven’t already, set up an Azure ML workspace.
- Set Up Your Environment: Install the Azure CLI and the Azure ML extension:
az extension add -n ml
- Create a Batch Endpoint: Use the Azure CLI or Python SDK to create a batch endpoint. Here’s an example using the CLI:
az ml batch-endpoint create --name <your-endpoint-name> --file <your-endpoint-config-file>
- Deploy Your Model: Create a deployment for your batch endpoint using your custom Docker image. Here’s an example configuration:
name:
- Test Your Deployment: Once deployed, you can test your batch endpoint to ensure everything works as expected.
References:
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